def main():
print("-- Welcome to movie-recommend! --")
# for output readability
np.set_printoptions(formatter={"float_kind": "{:25f}".format})
# baseline predictor by default
mode = BASELINE
# read command-line argument, if provided
if len(sys.argv) > 1:
if sys.argv[1] == IMPROVED or sys.argv[1] == BASELINE:
mode = sys.argv[1]
print(" You chose: {} predictor!".format(mode))
else:
print(
" {} is not a valid argument. Default: {} predictor!".format(
sys.argv[1], mode
)
)
else:
print(
" You did not provide any arguments. Default: {} predictor!".format(mode)
)
# read and parse text files
parser = Parser(mode)
print(" Parser initialized:")
print(
" {} test points and {} training points".format(
len(parser.test_set), np.count_nonzero(parser.training_matrix)
)
)
# initialize predictor and calculate rmse
predictor = Predictor(mode, parser.training_matrix, parser.test_set)
print(" rmse on test data (baseline): {}".format(predictor.rmse_test))
if predictor.mode == BASELINE:
print(
" rmse on training data (baseline): {}".format(predictor.rmse_training)
)
else:
print(
" rmse on test data (improved): {}".format(predictor.rmse_test_improved)
)
# execute histogram plotting and get error distribution
error_dist = (
predictor.calculate_absolute_errors(parser.test_set, predictor.improved_matrix)
if predictor.mode == IMPROVED
else predictor.calculate_absolute_errors(
parser.test_set, predictor.baseline_matrix
)
)
print(" Histogram saved to file. Error distribution: {}".format(error_dist))
